class_docstr: EfficientNet is a very efficient image-classification model. Trained on ImageNet.
  
  Attributes:
      model_path: Any efficientnet model (smaller to bigger) from efficientnet-b0 to efficientnet-b7
      init_model: Callable that initialises the model from the model_path
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      device: Device for model. Defaults to "cuda" if available.
code: def image_classification(self, image, top_k=10000):
      # TODO: Proper batching
      is_list = False
  
      if type(image) == list:
          is_list = True
  
      if not is_list:
          image = [image]
  
      probabilities = []
  
      for image in image:
          image = self.tfms(image).unsqueeze(0).to(self._model_device)
  
          logits = self.model(image)
  
          # TODO: Handle multi label
          dist = torch.softmax(logits, dim=1).squeeze(0).tolist()
  
          probs = {}
          for idx, prob in enumerate(dist):
              label = self.labels[idx]
  
              probs[label] = prob
  
          probs = sorted(
              probs.items(), key=lambda x: x[1], reverse=True
          )
          probs = {k: v for k, v in probs[:top_k]}
  
          probabilities.append(probs)
  
      if is_list == False:
          probabilities = probabilities[0]
  
      return probabilities
  
imports: import torch
  from PIL import Image
  from typing import Union, List, Dict
  from efficientnet_pytorch import EfficientNet
  from torchvision import transforms
  from torchvision.datasets import ImageFolder
  from random import shuffle
  from backprop.models import PathModel
  from backprop.utils.download import download
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.utils.data import DataLoader
  from functools import partial
  import torch.nn.functional
  import pytorch_lightning
  import json
  import os
  from io import BytesIO
  import base64
  from backprop.utils.helpers import base64_to_img
label_desc: Use image node_id 6 for calling the EfficientNet obj's underlying member method code with example usage: obj.image_classification(image, top_k) and returns: probabilities